Data quality issues can have a significant impact on businesses, resulting in operational inefficiency, decision paralysis, and customer experience degradation. Common data quality issues include incomplete data, duplicate data, inaccurate data, inconsistent data, outdated data, invalid data, schema changes, and orphaned data. These issues can cause wasted resources, lost revenue, and damaged brand reputation. To address these issues, businesses need systematic approaches to detect and resolve data quality problems before they impact business outcomes. Data observability tools can help improve data quality by providing automated anomaly detection, end-to-end lineage tracking, schema change monitoring, data freshness tracking, quality metric trending, cross-system consistency checking, and more. By taking control of data quality with a comprehensive observability toolkit like Metaplane, businesses can build trust in their data and focus on driving business value instead of putting out fires.